Structure-Based Optimization of a Potent PACE4 Inhibitor Containing a Decarboxylated P1 Arginine Mimetic
Bibliographic record
Abstract
Our recent studies have provided direct evidence for the critical role of PACE4 in the progression of prostrate cancer, identifying this enzyme as a promising target to design novel and effective treatments [1]. Moreover, we developed a potent PACE4 inhibitor with considerable selectivity (20-fold over furin) known as the Multi-Leu (ML) peptide [2]. In order to improve its pharmacological profile, we performed structure-activity relationship (SAR) studies and determined that the incorporation of the decarboxylated arginine mimetic (4-amidinobenzylamide, Amba) at the P1 position led to a more potent and stable analog [3]. Unfortunately, this inhibitor suffered from a reduced selectivity towards PACE4. To restore its specificity profile, we used a positional-scanning approach and synthesized peptide libraries by substituting each amino acid residue in the leucine core of our inhibitor. These studies revealed that we are able to enhance the specificity profile (3-fold) and preserve the inhibitory activity as well as antiproliferative properties of our inhibitor by incorporating a leucine isomer – L-isoleucine into its structure (Maluch, et al., unpublished data). Based on these results, we decided to perform further SAR studies aiming to improve the specificity and activity of our MLAmba inhibitor. We focused on the leucine core (P8-P5) and its modification with unnatural amino acid residues possessing hydrophobic character (Figure 1). First we evaluated the impact of a single substitution (from the P8 to P5 position) on the inhibitory activity of the resulting peptides, and then we combined the most promising modifications. In this work, we present the synthesis and biological evaluation of a new series of MLAmba analogs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".